Cross-Domain Face Sketch Synthesis

Zhang, Mingjin; Zhang, Jing; Yuan, Chi; Li, Yunsong; Wang, Nannan; Gao, Xinbo

doi:10.1109/access.2019.2931012

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…The proposed models for sketch to photo conversion can be divided into two main groups: before-GANs (or shallow learning-based) methods and GANs-based (or deep learning-based) methods [8], the latter utilizes deep neural networks for image synthesis while the former employs other machine learning techniques. In the following subsections, the trends and prominent works of each category are reviewed.…”

Section: Related Workmentioning

confidence: 99%

Face sketch to photo translation using generative adversarial networks

Farid¹,

Fard²,

Nickabadi³

2021

Preprint

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Translating face sketches to photo-realistic faces is an interesting and essential task in many applications like law enforcement and the digital entertainment industry. One of the most important challenges of this task is the inherent differences between the sketch and the real image such as the lack of color and details of the skin tissue in the sketch. With the advent of adversarial generative models, an increasing number of methods have been proposed for sketch-to-image synthesis. However, these models still suffer from limitations such as the large number of paired data required for training, the low resolution of the produced images, or the unrealistic appearance of the generated images. In this paper, we propose a method for converting an input facial sketch to a colorful photo without the need for any paired dataset. To do so, we use a pre-trained face photo generating model to synthesize high-quality natural face photos and employ an optimization procedure to keep high-fidelity to the input sketch. We train a network to map the facial features extracted from the input sketch to a vector in the latent space of the face generating model. Also, we study different optimization criteria and compare the results of the proposed model with those of the state-of-the-art models quantitatively and qualitatively. The proposed model achieved 0.655 in the SSIM index and 97.59% rank-1 face recognition rate with higher quality of the produced images.

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Section: Related Workmentioning

confidence: 99%

Face sketch to photo translation using generative adversarial networks

Farid¹,

Fard²,

Nickabadi³

2021

Preprint

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“…Face sketch synthesis has been studied for a long time due to its wide application. It plays an essential role in digital entertainment [1] and law enforcement based on video surveillance [2]. In law enforcement and criminal cases, the intelligent security system [3] can automatically retrieve photos of suspects from the police face database, so that the judicial authorities can quickly narrow down the scope of potential suspects.…”

Section: Introductionmentioning

confidence: 99%

MHGAN: Multi-Hierarchies Generative Adversarial Network for High-Quality Face Sketch Synthesis

Zhou

Cao

et al. 2020

IEEE Access

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Face sketch synthesis has made significant progress in the past few years. Recently, GANbased methods have shown promising results on image-to-image translation problems, especially phototo-sketch synthesis. Because the facial sketch has a hyper-abstract style and continuous graphic elements, compared with other image styles, its local details are easier to expose small artifacts and blur. The existing face sketch synthesis methods lack models for specific facial regions and usually generate face sketches with coarse structures. To synthesis high-quality sketches and overcome the blurs and deformations, this paper proposes a novel Multi-Hierarchies GAN, which divides the face image into multiple hierarchical structures to learn different regions' features of the face. It includes three modules: a local region module, mask module, and fusion module. The local region module can learn the detailed features of different local regions of the face by GAN. The mask module can generate a coarse facial structure of a sketch and uses the facial feature extractor to enhance the high-level image and learn the latent spaces' feature. The fusion module can generate the final sketch by combining fine local regions and coarse facial structure. Extensive qualitative and quantitative experiments illustrate that the proposed method outperforms the state-of-the-art methods on the CUFS and CUFSF standard datasets and photos on the internet. INDEX TERMS face sketch synthesis; generative adversarial network; facial feature extractor; Multi-Hierarchies GAN.

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An extensive survey on traditional and deep learning-based face sketch synthesis models

Balayesu

Kalluri

2019

Int. j. inf. tecnol.

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Cross-Domain Face Sketch Synthesis

Cited by 6 publications

References 38 publications

Face sketch to photo translation using generative adversarial networks

Face sketch to photo translation using generative adversarial networks

MHGAN: Multi-Hierarchies Generative Adversarial Network for High-Quality Face Sketch Synthesis

An extensive survey on traditional and deep learning-based face sketch synthesis models

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